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利用成交量加权内在熵估计指数波动率

文章 arXiv papers · 作者: Claudiu Vinte et al.
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总结

本文提出一种内在熵模型，用于估计股票市场指数的历史波动率。与仅使用每日开盘价、最高价、最低价和收盘价的估计方法不同，该方法还纳入成交量。该方法改编了早期的日内模型，将每日价格水平与分析期间当日成交量占总成交量的比例联系起来，并将该比例视为市场对相应价格水平的认可度。

作者使用多个US和亚洲指数的历史日度数据计算估计值，并在多个时间范围内与行业常用估计方法进行比较。他们报告称，该熵模型产生了被其描述为可靠的估计值，其变异系数尤高，数值范围显著低于其他先进估计方法。摘要没有提供详细的验证设计，也没有说明这些估计值在交易或衍生品定价中的表现，因此不应将所报告的比较视为其具备预测或投资价值的证明。

核心观点

  • 内在熵估计方法在每日OHLC价格之外还使用成交量。
  • 每日成交量占比被视为市场对相应价格水平的认可度。
  • 模型使用US和亚洲股票市场指数的历史数据进行评估。
  • 该模型的估计值在多个时间范围内与成熟的波动率估计方法进行比较。
  • 据报告，该模型的估计值变异较高，且低于其他先进估计方法的结果。

标签

股票波动率统计学美国市场中国市场
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# A Volatility Estimator of Stock Market Indices Based on the Intrinsic Entropy Model


# A Volatility Estimator of Stock Market Indices Based on the Intrinsic Entropy Model









Grasping the historical volatility of stock market indices and accurately estimating are two of the major focuses of those involved in the financial securities industry and derivative instruments pricing. This paper presents the results of employing the intrinsic entropy model as a substitute for estimating the volatility of stock market indices. Diverging from the widely used volatility models that take into account only the elements related to the traded prices, namely the open, high, low, and close prices of a trading day (OHLC), the intrinsic entropy model takes into account the traded volumes during the considered time frame as well. We adjust the intraday intrinsic entropy model that we introduced earlier for exchange-traded securities in order to connect daily OHLC prices with the ratio of the corresponding daily volume to the overall volume traded in the considered period. The intrinsic entropy model conceptualizes this ratio as entropic probability or market credence assigned to the corresponding price level. The intrinsic entropy is computed using historical daily data for traded market indices (S&P 500, Dow 30, NYSE Composite, NASDAQ Composite, Nikkei 225, and Hang Seng Index). We compare the results produced by the intrinsic entropy model with the volatility estimates obtained for the same data sets using widely employed industry volatility estimators. The intrinsic entropy model proves to consistently deliver reliable estimates for various time frames while showing peculiarly high values for the coefficient of variation, with the estimates falling in a significantly lower interval range compared with those provided by the other advanced volatility estimators.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。